refactor quant models loader and add support of OPT

modules/quant_loader.py

@@ -0,0 +1,51 @@
+import sys
+from pathlib import Path
+
+import accelerate
+import torch
+
+import modules.shared as shared
+
+sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
+
+
+# 4-bit LLaMA
+def load_quant(model_name, model_type):
+    if model_type == 'llama':
+        from llama import load_quant
+    elif model_type == 'opt':
+        from opt import load_quant
+    else:
+        print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
+        exit()
+
+    path_to_model = Path(f'models/{model_name}')
+    pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt'
+
+    # Try to find the .pt both in models/ and in the subfolder
+    pt_path = None
+    for path in [Path(p) for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
+        if path.exists():
+            pt_path = path
+
+    if not pt_path:
+        print(f"Could not find {pt_model}, exiting...")
+        exit()
+
+    model = load_quant(path_to_model, str(pt_path), shared.args.gptq_bits)
+
+    # Multiple GPUs or GPU+CPU
+    if shared.args.gpu_memory:
+        max_memory = {}
+        for i in range(len(shared.args.gpu_memory)):
+            max_memory[i] = f"{shared.args.gpu_memory[i]}GiB"
+        max_memory['cpu'] = f"{shared.args.cpu_memory or '99'}GiB"
+
+        device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LLaMADecoderLayer"])
+        model = accelerate.dispatch_model(model, device_map=device_map)
+
+    # Single GPU
+    else:
+        model = model.to(torch.device('cuda:0'))
+
+    return model